Use of multi-electrode array recordings in studies of network synaptic plasticity in both time and space

Multielectrode array use in insect auditory neuroscience to unravel the spatio-temporal response pattern in the prothoracic ganglion of Mecopoda elongata

Mechanoreceptors in hearing organs transduce sound-induced mechanical responses into neuronal signals, which are further processed and forwarded to the brain along a chain of neurons in the auditory pathway. Bushcrickets (katydids) have their ears in the front leg tibia, and the first synaptic integration of sound-induced neuronal signals takes place in the primary auditory neuropil of the prothoracic ganglion. By combining intracellular recordings of the receptor activity in the ear, extracellular multichannel array recordings on top of the prothoracic ganglion and hook electrode recordings at the neck connective, we mapped the timing of neuronal responses to tonal sound stimuli along the auditory pathway from the ears towards the brain. The use of the multielectrode array allows the observation of spatio-temporal patterns of neuronal responses within the prothoracic ganglion. By eliminating the sensory input from one ear, we investigated the impact of contralateral projecting interneurons in the prothoracic ganglion and added to previous research on the functional importance of contralateral inhibition for binaural processing. Furthermore, our data analysis demonstrates changes in the signal integration processes at the synaptic level indicated by a long-lasting increase in the local field potential amplitude. We hypothesize that this persistent increase of the local field potential amplitude is important for the processing of complex signals, such as the conspecific song.

Increased Dentate Gyrus Excitability in the Intrahippocampal Kainic Acid Mouse Model for Temporal Lobe Epilepsy

The intrahippocampal kainic acid (IHKA) mouse model is an extensively used in vivo model to investigate the pathophysiology of mesial temporal lobe epilepsy (mTLE) and to develop novel therapies for drug-resistant epilepsy. It is characterized by profound hippocampal sclerosis and spontaneously occurring seizures with a major role for the injected damaged hippocampus, but little is known about the excitability of specific subregions. The purpose of this study was to electrophysiologically characterize the excitability of hippocampal subregions in the chronic phase of the induced epilepsy in the IHKA mouse model. We recorded field postsynaptic potentials (fPSPs) after electrical stimulation in the CA1 region and in the dentate gyrus (DG) of hippocampal slices of IHKA and healthy mice using a multielectrode array (MEA). In the DG, a significantly steeper fPSP slope was found, reflecting higher synaptic strength. Population spikes were more prevalent with a larger spatial distribution in the IHKA group, reflecting a higher degree of granule cell output. Only minor differences were found in the CA1 region. These results point to increased neuronal excitability in the DG but not in the CA1 region of the hippocampus of IHKA mice. This method, in which the excitability of hippocampal slices from IHKA mice is investigated using a MEA, can now be further explored as a potential new model to screen for new interventions that can restore DG function and potentially lead to novel therapies for mTLE.

Development of recycled and miniaturized electroanalytical sensor: Probing isoniazid determination in environmental water matrices

Surface water pollution has become relevant because growing population and intense industrial activities. Thus, to protect the environment from contamination, recently the electroanalytical sensors that require small sample volume and easy preparation have shown a prominent performance for pharmaceuticals monitoring. For this purpose, a miniaturized electrochemical platform was developed based on recycling obsolete computer integrated circuits (microchips), fitting with the ideals of green chemistry and circular economy. The gold microelectrodes array (Au-μEA) was easily exposed by polishing the device surface and then characterized by optical microscopy, scanning electron microscopy and cyclic voltammetry. To enhance the analytical performance for isoniazid detection, the Au-μEA was modified with electrochemically reduced graphene oxide (ERGO). The developed sensor presented a linear range between 5 and 100 μmol L-1 and a limit of detection of 1.38 μmol L-1 demonstrating a reliable performance. Looking to its environmental application, the ERGO/Au-μEA sensor was used for isoniazid quantification in lagoon, river, tap water and synthetic effluent spiked samples with recovery values between 92.5 and 108.4%. Thus, this research field opens up new possibilities in global water-related issues contributing with innovative sustainable solutions.

Measuring Synchronization between Spikes and Local Field Potential Based on the Kullback-Leibler Divergence

Neurophysiological studies have shown that there is a close relationship between spikes and local field potential (LFP), which reflects crucial neural coding information. In this paper, we used a new method to evaluate the synchronization between spikes and LFP. All possible phases of LFP from −π to π were first binned into a freely chosen number of bins; then, the probability of spikes falling in each bin was calculated, and the deviation degree from the uniform distribution based on the Kullback–Leibler divergence was calculated to define the synchronization between spikes and LFP. The simulation results demonstrate that the method is rapid, basically unaffected by the total number of spikes, and can adequately resist the noise of spike trains. We applied this method to the experimental data of patients with intractable epilepsy, and we observed the synchronization between spikes and LFP in the formation of memory. These results show that our proposed method is a powerful tool that can quantitatively measure the synchronization between spikes and LFP.

Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

The synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we show for the first time that STC mechanisms modify the storage of memories such that after several hours memory recall is significantly improved. We identify two contributing processes: a merely time-dependent passive improvement, and an active improvement during recall. The described characteristics can provide a new principle for storing information in biological and artificial neural circuits.

Microelectrode array analysis of mouse uterine smooth muscle electrical activity†

Uterine contractions are important for various functions of the female reproductive cycle. Contractions are generated, in part, by electrical coupling of smooth muscle cells of the myometrium, the main muscle layer of the uterus. Aberrant myometrial electrical activity can lead to uterine dysfunction. To better understand and treat conditions associated with aberrant activity, it is crucial to understand the mechanisms that underlie normal activity. Here, we used microelectrode array (MEA) to simultaneously record and characterize myometrial electrical activities at high spatial and temporal resolution. Mouse myometrial longitudinal muscle tissue was isolated at different stages throughout the estrous cycle and placed on an 8×8 MEA. Electrical activity was recorded for 10 min at a sampling rate of 12.5 kHz. We used a spike-tracking algorithm to independently analyze each channel and developed a pipeline to quantify the amplitude, duration, frequency, and synchronicity of the electrical activities. Electrical activities in estrous were more synchronous, and had shorter duration, higher frequency, and lower amplitude than electrical activities in non-estrous. We conclude that MEA can be used to detect differential patterns of myometrial electrical activity in distinct estrous cycle stages. In the future, this methodology can be used to assess different physiological and pathological states and evaluate therapeutic agents that regulate uterine function.

Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method

Background

It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network functions.

Results

In this study, the ideas of linear Granger causality modeling and causality identification are extended to those of nonlinear Granger causality modeling and network structure identification. We employed Radial Basis Functions to fit the nonlinear multivariate dynamical responses of BNNs with neuronal pulse firing. By introducing the contributions from presynaptic neurons and detecting whether the predictions for postsynaptic neurons’ pulse firing signals are improved or not, we can reveal the information flows distribution of BNNs. Thus, the functional connections from presynaptic neurons can be identified from the obtained network information flows. To verify the effectiveness of the proposed method, the Nonlinear Granger Causality Identification Method (NGCIM) is applied to the network structure discovery processes of Spiking Neural Networks (SNN). SNN is a simulation model based on an Integrate-and-Fire mechanism. By network simulations, the multi-channel neuronal pulse sequence data of the SNNs can be used to reversely identify the synaptic connections and strengths of the SNNs.

Conclusions

The identification results show: for 2–6 nodes small-scale neural networks, 20 nodes medium-scale neural networks, and 100 nodes large-scale neural networks, the identification accuracy of NGCIM with the Gaussian kernel function was 100%, 99.64%, 98.64%, 98.37%, 98.31%, 84.87% and 80.56%, respectively. The identification accuracies were significantly higher than those of a traditional Linear Granger Causality Identification Method with the same network sizes. Thus, with an accumulation of the data obtained by the existing measurement methods, such as Electroencephalography, functional Magnetic Resonance Imaging, and Multi-Electrode Array, the NGCIM can be a promising network modeling method to infer the functional connective maps of BNNs.

Combining super-resolution microscopy with neuronal network recording using magnesium fluoride thin films as cover layer for multi-electrode array technology

We present an approach for fabrication of reproducible, chemically and mechanically robust functionalized layers based on MgF₂ thin films on thin glass substrates. These show great advantages for use in super-resolution microscopy as well as for multi-electrode-array fabrication and are especially suited for combination of these techniques. The transparency of the coated substrates with the low refractive index material is adjustable by the layer thickness and can be increased above 92%. Due to the hydrophobic and lipophilic properties of the thin crystalline MgF₂ layers, the temporal stable adhesion needed for fixation of thin tissue, e.g. cryogenic brain slices is given. This has been tested using localization-based super-resolution microscopy with currently highest spatial resolution in light microscopy. We demonstrated that direct stochastic optical reconstruction microscopy revealed in reliable imaging of structures of central synapses by use of double immunostaining of post- (homer1 and GluA2) and presynaptic (bassoon) marker structure in a 10 µm brain slice without additional fixing of the slices. Due to the proven additional electrical insulating effect of MgF₂ layers, surfaces of multi-electrode-arrays were coated with this material and tested by voltage-current-measurements. MgF₂ coated multi-electrode-arrays can be used as a functionalized microscope cover slip for combination with live-cell super-resolution microscopy.

Heterozygous loss of function of IQSEC2/Iqsec2 leads to increased activated Arf6 and severe neurocognitive seizure phenotype in females

Clinical presentations of mutations in the IQSEC2 gene on the X-chromosome initially implicated to cause non-syndromic intellectual disability (ID) in males have expanded to include early onset seizures in males as well as in females. The molecular pathogenesis is not well understood, nor the mechanisms driving disease expression in heterozygous females. Using a CRISPR/Cas9-edited Iqsec2 KO mouse model, we confirm the loss of Iqsec2 mRNA expression and lack of Iqsec2 protein within the brain of both founder and progeny mice. Both male (52%) and female (46%) Iqsec2 KO mice present with frequent and recurrent seizures. Focusing on Iqsec2 KO heterozygous female mice, we demonstrate increased hyperactivity, altered anxiety and fear responses, decreased social interactions, delayed learning capacity and decreased memory retention/novel recognition, recapitulating psychiatric issues, autistic-like features, and cognitive deficits present in female patients with loss-of-function IQSEC2 variants. Despite Iqsec2 normally acting to activate Arf6 substrate, we demonstrate that mice modelling the loss of Iqsec2 function present with increased levels of activated Arf6. We contend that loss of Iqsec2 function leads to altered regulation of activated Arf6-mediated responses to synaptic signalling and immature synaptic networks. We highlight the importance of IQSEC2 function for females by reporting a novel nonsense variant c.566C > A, p.(S189*) in an elderly female patient with profound intellectual disability, generalised seizures, and behavioural disturbances. Our human and mouse data reaffirm IQSEC2 as another disease gene with an unexpected X-chromosome heterozygous female phenotype. Our Iqsec2 mouse model recapitulates the phenotypes observed in human patients despite the differences in the IQSEC2/Iqsec2 gene X-chromosome inactivation between the species.

Inhomogeneities in Network Structure and Excitability Govern Initiation and Propagation of Spontaneous Burst Activity

The mesoscale architecture of neuronal networks strongly influences the initiation of spontaneous activity and its pathways of propagation. Spontaneous activity has been studied extensively in networks of cultured cortical neurons that generate complex yet reproducible patterns of synchronous bursting events that resemble the activity dynamics in developing neuronal networks in vivo. Synchronous bursts are mostly thought to be triggered at burst initiation sites due to build-up of noise or by highly active neurons, or to reflect reverberating activity that circulates within larger networks, although neither of these has been observed directly. Inferring such collective dynamics in neuronal populations from electrophysiological recordings crucially depends on the spatial resolution and sampling ratio relative to the size of the networks assessed. Using large-scale microelectrode arrays with 1024 electrodes at 0.3 mm pitch that covered the full extent of in vitro networks on about 1 cm², we investigated where bursts of spontaneous activity arise and how their propagation patterns relate to the regions of origin, the network's structure, and to the overall distribution of activity. A set of alternating burst initiation zones (BIZ) dominated the initiation of distinct bursting events and triggered specific propagation patterns. Moreover, BIZs were typically located in areas with moderate activity levels, i.e., at transitions between hot and cold spots. The activity-dependent alternation between these zones suggests that the local networks forming the dominating BIZ enter a transient depressed state after several cycles (similar to Eytan et al., 2003), allowing other BIZs to take over temporarily. We propose that inhomogeneities in the network structure define such BIZs and that the depletion of local synaptic resources limit repetitive burst initiation.

Advances in Nano Neuroscience: From Nanomaterials to Nanotools

During the last decades, neuroscientists have increasingly exploited a variety of artificial, de-novo synthesized materials with controlled nano-sized features. For instance, a renewed interest in the development of prostheses or neural interfaces was driven by the availability of novel nanomaterials that enabled the fabrication of implantable bioelectronics interfaces with reduced side effects and increased integration with the target biological tissue. The peculiar physical-chemical properties of nanomaterials have also contributed to the engineering of novel imaging devices toward sophisticated experimental settings, to smart fabricated scaffolds and microelectrodes, or other tools ultimately aimed at a better understanding of neural tissue functions. In this review, we focus on nanomaterials and specifically on carbon-based nanomaterials, such as carbon nanotubes (CNTs) and graphene. While these materials raise potential safety concerns, they represent a tremendous technological opportunity for the restoration of neuronal functions. We then describe nanotools such as nanowires and nano-modified MEA for high-performance electrophysiological recording and stimulation of neuronal electrical activity. We finally focus on the fabrication of three-dimensional synthetic nanostructures, used as substrates to interface biological cells and tissues in vitro and in vivo.

Recording and Modulation of Epileptiform Activity in Rodent Brain Slices Coupled to Microelectrode Arrays

Temporal lobe epilepsy (TLE) is the most common partial complex epileptic syndrome and the least responsive to medications. Deep brain stimulation (DBS) is a promising approach when pharmacological treatment fails or neurosurgery is not recommended. Acute brain slices coupled to microelectrode arrays (MEAs) represent a valuable tool to study neuronal network interactions and their modulation by electrical stimulation. As compared to conventional extracellular recording techniques, they provide the added advantages of a greater number of observation points and a known inter-electrode distance, which allow studying the propagation path and speed of electrophysiological signals. However, tissue oxygenation may be greatly impaired during MEA recording, requiring a high perfusion rate, which comes at the cost of decreased signal-to-noise ratio and higher oscillations in the experimental temperature. Electrical stimulation further stresses the brain tissue, making it difficult to pursue prolonged recording/stimulation epochs. Moreover, electrical modulation of brain slice activity needs to target specific structures/pathways within the brain slice, requiring that electrode mapping be easily and quickly performed live during the experiment. Here, we illustrate how to perform the recording and electrical modulation of 4-aminopyridine (4AP)-induced epileptiform activity in rodent brain slices using planar MEAs. We show that the brain tissue obtained from mice outperforms rat brain tissue and is thus better suited for MEA experiments. This protocol guarantees the generation and maintenance of a stable epileptiform pattern that faithfully reproduces the electrophysiological features observed with conventional field potential recording, persists for several hours, and outlasts sustained electrical stimulation for prolonged epochs. Tissue viability throughout the experiment is achieved thanks to the use of a small-volume custom recording chamber allowing for laminar flow and quick solution exchange even at low (1 mL/min) perfusion rates. Quick MEA mapping for real-time monitoring and selection of stimulating electrodes is performed by a custom graphic user interface (GUI).

Action potential propagation recorded from single axonal arbors using multielectrode arrays

We report the presence of co-occurring extracellular action potentials (eAPs) from cultured mouse hippocampal neurons among groups of planar electrodes on multielectrode arrays (MEAs). The invariant sequences of eAPs among coactive electrode groups, repeated co-occurrences, and short interelectrode latencies are consistent with action potential propagation in unmyelinated axons. Repeated eAP codetection by multiple electrodes was widespread in all our data records. Codetection of eAPs confirms they result from the same neuron and allows these eAPs to be isolated from all other spikes independently of spike sorting algorithms. We averaged co-occurring events and revealed additional electrodes with eAPs that would otherwise be below detection threshold. We used these eAP cohorts to explore the temperature sensitivity of action potential propagation and the relationship between voltage-gated sodium channel density and propagation velocity. The sequence of eAPs among coactive electrodes "fingerprints" neurons giving rise to these events and identifies them within neuronal ensembles. We used this property and the noninvasive nature of extracellular recording to monitor changes in excitability at multiple points in single axonal arbors simultaneously over several hours, demonstrating independence of axonal segments. Over several weeks, we recorded changes in interelectrode propagation latencies and ongoing changes in excitability in different regions of single axonal arbors. Our work illustrates how repeated eAP co-occurrences can be used to extract physiological data from single axons with low-density MEAs. However, repeated eAP co-occurrences lead to oversampling spikes from single neurons and thus can confound traditional spike-train analysis. NEW & NOTEWORTHY We studied action potential propagation in single axons using low-density multielectrode arrays. We unambiguously identified the neuronal sources of propagating action potentials and recorded extracellular action potentials from several positions within single axonal arbors. We found a surprisingly high density of axonal voltage-gated sodium channels responsible for a high propagation safety factor. Our experiments also demonstrate that excitability in different segments of single axons is regulated independently on timescales from hours to weeks.

A biopotential optrode array: operation principles and simulations

We propose an optical electrode ’optrode’ sensor array for biopotential measurements. The transduction mechanism is based on deformed helix ferroelectric liquid crystals which realign, altering the optrode’s light reflectance properties, relative to applied potential fields of biological cells and tissue. A computational model of extracellular potential recording by the optrode including the electro-optical transduction mechanism is presented, using a combination of time-domain and frequency-domain finite element analysis. Simulations indicate that the device has appropriate temporal response to faithfully transduce neuronal spikes, and spatial resolution to capture impulse propagation along a single neuron. These simulations contribute to the development of multi-channel optrode arrays for spatio-temporal mapping of electric events in excitable biological tissue.

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

Acid-sensing ion channel 1a contributes to hippocampal LTP inducibility through multiple mechanisms

The exact roles of acid-sensing ion channels (ASICs) in synaptic plasticity remain elusive. Here, we address the contribution of ASIC1a to five forms of synaptic plasticity in the mouse hippocampus using an in vitro multi-electrode array recording system. We found that genetic deletion or pharmacological blockade of ASIC1a greatly reduced, but did not fully abolish, the probability of long-term potentiation (LTP) induction by either single or repeated high frequency stimulation or theta burst stimulation in the CA1 region. However, these treatments did not affect hippocampal long-term depression induced by low frequency electrical stimulation or (RS)-3,5-dihydroxyphenylglycine. We also show that ASIC1a exerts its action in hippocampal LTP through multiple mechanisms that include but are not limited to augmentation of NMDA receptor function. Taken together, these results reveal new insights into the role of ASIC1a in hippocampal synaptic plasticity and the underlying mechanisms. This unbiased study also demonstrates a novel and objective way to assay synaptic plasticity mechanisms in the brain.

Spatial and temporal plasticity of synaptic organization in anterior cingulate cortex following peripheral inflammatory pain: multi-electrode array recordings in rats

To explore whether experiencing inflammatory pain has an impact upon intracortical synaptic organization, the planar multi-electrode array (MEA) technique and 2-dimensional current source density (2D-CSD) imaging were used in slice preparations of the anterior cingulate cortex (ACC) from rats. Synaptic activity across different layers of the ACC was evoked by deep layer stimulation through one electrode. The layer-localization of both local field potentials (LFPs) and the spread of current sink calculated by 2D-CSD analysis was characterized pharmacologically. Moreover, the induction of long-term potentiation (LTP) and changes in LTP magnitude were also evaluated. We found that under naïve conditions, the current sink was initially generated in layer VI, then spread to layer V and finally confined to layers II-III. This spatial pattern of current sink movement typically reflected changes in depolarized sites from deep layers (V-VI) to superficial layers (II-III) where intra- and extracortical inputs terminate. In the ACC slices from rats in an inflamed state (for 2 h) caused by intraplantar bee-venom injection, the spatial profile of intra-ACC synaptic organization was significantly changed, showing an enlarged current sink distribution and a leftward shift of the stimulus-response curves relative to the naïve and saline controls. The change was more distinct in the superficial layers (II-III) than in the deep site. In terms of temporal properties, the rate of LTP induction was significantly increased in layers II-III by inflammatory pain. However, the magnitude of LTP was not significantly enhanced by this treatment. Taken together, these results show that inflammatory pain results in distinct spatial and temporal plasticity of synaptic organization in the ACC, which may lead to altered synaptic transmission and modulation.

Understanding the temporal evolution of neuronal connectivity in cultured networks using statistical analysis

BACKGROUND

Micro-Electrode Array (MEA) technology allows researchers to perform long-term non-invasive neuronal recordings in-vitro while actively interacting with the cultured neurons. Despite numerous studies carried out using MEAs, many functional, chemical and structural mechanisms of how dissociated cortical neurons develop and respond to external stimuli are not yet well understood because of the lack of quantitative studies that assess how their development can be affected by chronic external stimulation.

METHODS

To investigate network changes, we analyzed a large MEA data set composed of neuron spikes recorded from cultures of dissociated rat cortical neurons plated on MEA dishes with 59 recording electrodes each. Neural network activity was recorded during the first five weeks of each culture's in-vitro development. Stimulation sessions were delivered to each of the 59 electrodes. The False Discovery Rate technique was used to quantify the temporal evolution of dissociated cortical neurons. Our analysis focused on network responses that occurred within selected time window durations, namely 50 ms, 100 ms and 150 ms after stimulus onset.

RESULTS

Our results show an evolution in dissociated cortical neuronal network activity over time, that reflects the network synaptic evolution. Furthermore, we tested the sensitivity of our technique to different observation time windows and found that varying the time windows, allows us to capture different dynamics of the observed responses. In addition, when selecting a 150 ms observation time window, our findings indicate that cultures dissociated from the same brain tissue display trends in their temporal evolution that are more similar than those obtained from different brains.

CONCLUSION

Our results emphasize that the FDR technique can be implemented without the need to make any particular assumptions about the data a priori. The proposed technique was able to capture the well-known dissociated cortical neuron networks' temporal evolution, that has been previously observed in in-vivo and in intact brain tissue studies. Furthermore, our findings suggest that the time window that is used to capture the stimulus-evoked network responses is a critical parameter to analyze the electrical behavioral and temporal evolution of dissociated cortical neurons.